A subscription business wants customers to pause a recurring delivery. The interface needs a pause button, but the change also depends on billing dates, warehouse cut-offs, stock reservations, and the rules for resuming. Generating the button and endpoint does not settle those decisions.
An AI-assisted lifecycle can help the team investigate those dependencies, develop a bounded implementation, and prepare the evidence needed to release and operate it. The useful unit of work is the supported business change, including the decisions and checks around its code.
AI-DLC stands for AI-Driven Development Lifecycle. AWS’s description groups its methodology into Inception, Construction, and Operations, with collaborative practices called Mob Elaboration and Mob Construction. AI helps develop proposals and artefacts while people validate decisions. The following example applies that broad approach without assuming every project needs the same workflow or tools.
Establish the Intent and Its Unanswered Questions
For the pause feature, establish the customer outcome first. Does pausing stop the next shipment, the next billing attempt, or both? Can customers pause indefinitely? What happens if the warehouse has already begun work? Product, operations, and finance may each hold part of the answer.
AI can inspect existing code and documents to identify likely rules and conflicting implementations. Keep those findings separate from decisions. A branch that currently skips billing may reveal behaviour worth investigating, but it does not prove that this behaviour is the approved policy for the new feature.
Record agreed rules, assumptions, and unresolved questions in a place linked to the change. Give consequential questions an owner. The team can proceed on settled work while avoiding dependent implementation that would turn an unresolved choice into an apparent requirement.
Acceptance examples make the intent concrete. A request before the cut-off should produce the agreed paused state; one after the cut-off may affect a later cycle. A customer should not be able to pause another customer’s subscription by changing an identifier. These examples become references for design and review.
Build a Useful Map of the Existing System
The agent needs enough repository context to identify the relevant state and integration boundaries. That may include the subscription service, billing scheduler, warehouse adapter, customer permissions, and background jobs. Finding matching words is a beginning, rather than a complete impact analysis.
Ask what owns each decision. The subscription record may own the requested pause, while the fulfilment service knows whether a shipment can still be stopped. A billing schedule can contain an already submitted operation whose outcome cannot be changed merely by updating the subscription flag.
Trace one ordinary cycle and one boundary case through the current system. Identify inputs, authoritative state, external effects, and recovery steps. This gives the proposed design a more useful foundation than a diagram generated from class names alone.
Keep the context selective and current. A short decision record that identifies the applicable rule and its source is more useful than a directory of overlapping summaries. If the team changes the cut-off policy, update the requirement, examples, and affected implementation plan together.
Design the State Transition and Its Failure Cases
A pause operation needs defined states and transitions. Distinguish requested, applied for a particular cycle, rejected, and uncertain outcomes where those distinctions matter. A Boolean may be sufficient for some systems, but it should not conceal in-flight work that still needs resolution.
Compare designs against existing ownership and recovery needs. A synchronous call can give immediate confirmation but depends on the remote service being available. A queued request can preserve intent during an outage while requiring a visible pending state. An orchestrator can manage several steps, with its own persistence and effect-handling contract.
AI can outline alternatives and locate established patterns. The team should select a design using the system’s actual constraints, including support experience and the consequences of partial completion. An attractive diagram is not evidence that the chosen boundaries preserve the required invariants.
| Scenario | Decision to establish | Evidence to plan |
|---|---|---|
| Pause before the cut-off | Which cycle is affected | Boundary-time acceptance cases |
| Warehouse already processing | Whether later work can still stop | Integration outcome and customer message |
| Two simultaneous requests | Which state transition is allowed | Concurrency check |
| Billing response times out | How the outcome is reconciled | Stable operation reference and recovery path |
| Resume after a long pause | Which current rules apply | Historical and current-state cases |
The design should also say what the customer sees while work is pending. Reporting success before the required downstream decision is confirmed can create a misleading promise, even if the background process eventually converges.
Construct a Change That Can Be Reviewed
Divide implementation into coherent units with clear acceptance criteria. A contract change, persistence update, and adapter modification may be separate patches or one closely related patch, depending on the dependency. The objective is a change a reviewer can understand, not a fixed number of files.
Give the agent established commands and relevant conventions. It can then use the compiler, linter, tests, and local tooling as feedback. Record failures and their interpretation rather than allowing an unavailable test environment to be described as a successful check.
Review new dependencies, error handling, and changes to surrounding behaviour. An implementation may satisfy the happy path by swallowing an exception or skipping a validation that another workflow relies on. The final diff needs to be assessed against the intended scope.
Avoid producing extra abstractions or documents merely because generation is easy. Each artefact should support a decision, implementation, verification, or operational need. Unused plans and parallel summaries can become stale context for the next change.
Verify the Interpretation Independently
Generated tests can improve coverage, but their expected results need scrutiny. A model that assumes a pause always affects the next shipment may generate both the implementation and tests around that assumption. Passing them would not resolve the actual cut-off policy.
Use the agreed examples to challenge the code. Include permissions, repeated requests, concurrent state changes, late messages, and records created under earlier versions. Choose cases according to the feature’s consequences rather than mirroring each implementation branch mechanically.
Test appropriate integration boundaries too. A mock warehouse can confirm that the application calls an adapter, while a contract or integration check addresses the actual request and response behaviour. The distinctions between contract and integration testing help explain what each result establishes.
When a test fails, determine whether the implementation, requirement, fixture, or environment is wrong. Automatically changing the expected value to match the output converts a useful challenge into agreement. Record justified changes to the requirement as decisions rather than concealing them inside a test update.
Prepare Operation Before Release
A release plan needs compatible versions, rollout scope, monitoring, and recovery. If a new state can appear while old workers are still running, establish how those workers handle it. A feature flag does not by itself make a data-model change compatible or undo an already submitted billing operation.
AI can draft runbooks and deployment definitions, but the team needs evidence that the relevant commands and recovery paths work in its environment. An unexercised procedure should remain identified as such. Production validation can add evidence through controlled exposure after earlier checks.
Assign ownership for pending and failed pauses. Support staff need to see the requested cycle, confirmed state, and any outstanding external outcome. A generic error message and a trace identifier may help technical investigation while remaining insufficient to resolve the customer’s request.
Keep execution permissions aligned with the task. Preparing a release, applying a database migration, and repairing a live subscription have different consequences. Define the required authority and retain a record of the operation actually performed.
Keep a Useful Handover
Before closing the change, connect the accepted requirement, relevant design decisions, final diff, executed checks, and operational notes. A maintainer should be able to establish why a pause affects a particular cycle without searching an entire conversation. Retain unresolved limitations and their owners alongside the evidence, so a polished completion summary does not hide work that remains. This handover can be short when the change is small; completeness concerns the decisions needed later, rather than document volume.
Adapt the Workflow and Learn From Results
AWS’s adaptive workflow implementation describes adjusting process depth to context. A minor text correction does not need the full investigation appropriate to a cross-service billing change. Risk and uncertainty should determine where deeper work is useful.
After release, examine actual completion, pending work, customer corrections, and operational effort. A rapid implementation can still leave a costly support burden if the workflow handles uncertainty poorly. Feed confirmed findings back into requirements and checks so later changes inherit useful context.
Measure the method through accepted outcomes, review effort, and rework. The companion comparison of AI-DLC and existing SDLC practices considers that evaluation in more detail. Here the practical objective is continuity: the people operating the feature can connect its behaviour to decisions and evidence maintained throughout the change.
